Confidence Estimation Tool for Generative AI Root Cause Analysis
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Solution Overview
Problem
Existing analysis systems using generative AI models struggle to effectively quantify the level of confidence associated with system-generated root causes for events, particularly when the models 'hallucinate' and lack domain-specific knowledge.
Innovation Solution
A confidence estimation tool that uses a calibrated confidence mapping model to estimate confidence for model-generated candidate root causes by referencing relevant historical events, determining description-based and cause-based confidence scores, and mapping these scores to a final confidence score.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a generative AI model is used for root cause analysis, then the system can generate natural language predictions, but the model cannot effectively quantify confidence levels due to hallucinations and lack of domain-specific knowledge
Solution Approach 1:
The patent introduces a confidence estimation tool as an intermediary component between the generative AI model and the root cause analysis output. This tool receives the model's predictions and generates separate confidence scores, allowing the system to quantify uncertainty without modifying the underlying generative model. The confidence estimation tool acts as a mediator that translates model outputs into reliable confidence measurements.
Solution Approach 2:
The system implements feedback mechanisms where confidence scores are generated based on historical event data and model predictions. The confidence estimation process continuously learns from past performance, adjusting confidence calculations based on the model's track record in specific domains. This feedback loop enables the system to improve confidence quantification over time while maintaining the generative model's core functionality.
2Adaptability or versatility
If a large language model trained on general-domain data is used, then the model can handle a wide range of topics, but it produces inaccuracies when generating content in specific domains
Solution Approach 1:
The patent applies local quality by tailoring the confidence estimation process to specific target domains while maintaining the general-purpose nature of the underlying language model. The confidence estimation tool is configured with domain-specific parameters and historical data, allowing it to provide accurate confidence scores for particular domains without requiring the core language model to be retrained. This enables the system to maintain versatility across domains while achieving reliability in specific areas.
Solution Approach 2:
The system changes parameters related to confidence estimation based on the target domain being analyzed. Different domains have different confidence thresholds, historical event distributions, and evaluation criteria. The confidence estimation tool dynamically adjusts these parameters according to the specific domain context, allowing the general language model to maintain high accuracy across diverse domains by adapting the confidence assessment methodology rather than the core generation process.
3Measurement precision
If the confidence estimation tool references historical events to determine confidence scores, then the confidence calibration accuracy improves, but the processing time and computational complexity increase
Solution Approach 1:
The patent implements preliminary action by pre-processing and organizing historical event data into structured formats before they are needed for confidence estimation. Historical events are stored with pre-computed features and relevance metrics, allowing the confidence estimation tool to quickly retrieve and compare relevant historical cases without performing extensive computations in real-time. This pre-preparation significantly reduces processing time while maintaining accurate confidence calibration.
Solution Approach 2:
The system extracts only the most relevant features and characteristics from historical events when computing confidence scores, rather than analyzing complete historical records. The confidence estimation tool identifies and extracts key comparable elements between current and historical events, focusing computational resources on the most discriminative features. This extraction approach maintains measurement precision while reducing the overall computational burden and processing time.
Data Source
AI summary
A confidence estimation tool uses a calibrated confidence mapping model to estimate confidence for a model-generated candidate root cause. The tool uses a generative artificial intelligence (“AI”) model to determine, based on a description of a current event, a candidate root cause of the current event. The tool determines a description-based confidence score using the description of the current event and descriptions of a set of relevant historical events in a target domain. The tool also determines a cause-based confidence score using the candidate root cause of the current event and root causes of the set of relevant historical events. Finally, the tool determines a final confidence score using the description-based and cause-based confidence scores. Even if the generative AI model is configured for general-domain applications, by referencing relevant historical events, the tool can accurately estimate confidence for a model-generated candidate root cause within the target domain.